The application of digital twins provides value creation within the fields of operations and service management; existing research around decision-making and value co-creation is limited at this point. Prior studies have provided insights into the benefits of digital twins that combined both data and simulation approaches; however, there remains a managerial gap. The purpose of this paper is to explore this research gap using input from a multiple case study research design from both manufacturing environments and non-manufacturing environments. The authors use ten cases to explore how digital twins support value co-creation through decision-making. The authors were all involved in the development of the ten cases. Individual biases were removed by using the literature to provide the assessment dimensions and allowing a convergence of the results. Drawing on the lessons from the ten cases, this study empirically identified eight managerial issues that need to be considered when developing digital twins to support multi-stakeholder decision-making that leads to value co-creation. The application of digital twins in value co-creation and decision-making is a topic that has developed from practice and is an area where a research gap exists between theory and practice. A cross-case analysis was developed based on the literature and the ten cases (eight industrial and two pilot-scale cases) providing the empirical findings. The findings describe how firms can design, develop, and commercialize digital-twin-enabled value propositions and will initiate future research.

The goal of this paper is to further elaborate a new concept for value creation by decision support services in industrial service ecosystems using digital twins and to apply it to an extended case study. The aim of the original model was to design and integrate an architecture of digital twins derived from business needs that leveraged the potential of the synergies in the ecosystem. The conceptual framework presented in this paper extends the semantic ontology model for integrating the digital twins. For the original model, technical modeling approaches were developed and integrated into an ecosystem perspective based on a modeling of the ecosystem and the actors’ decision jobs. In a service ecosystem comprising several enterprises and a multitude of actors, decision making is based on the interlinkage of the digital twins of the equipment and the processes, which is achieved by the semantic ontology model further elaborated in this paper. The implementation of the digital twin architecture is shown in the example of a manufacturing SME (small and medium-sized enterprise) case that was introduced in. The mixed semantic modeling and model-based systems engineering for this implementation is discussed in further detail in this paper. The findings of this detailed study provide a theoretical concept for implementing digital twins on the level of service ecosystems and integrating digital twins based on a unified ontology. This provides a practical blueprint to companies for developing digital twin based services in their own operations and beyond in their ecosystem.

Lean production principles have greatly contributed to the efficient and customer-oriented mass production of goods and services. A core element of lean production is the focus on cycle times and designing production controls and buffers around any bottlenecks in the system. Hence, a production line organized by lean principles will operate in a static or at least quasi-static way. While the individualization of products is an interesting business approach, it can influence cycle times and in-time production. This work demonstrates how performance losses induced by highly variable cycle times can be recovered using a digital twin. The unit under analysis is an industrial joiner’s workshop. Due to the high variance in cycle time, the joinery fails its production target, even if all machines are below 80% usage. Using a discrete event simulation of the production line, different production strategies can be evaluated efficiently and systematically. It is successfully shown that the performance losses due to the highly variable cycle times can be compensated using a digital twin in combination with optimization strategies. This is achieved by operating the system in a non-static mode, exploiting the flexibilities within the systems.

Improving the energy efficiency of machine tools is one of the challenges regarding the European energy saving goals. This work presents a new tool, enabling an effective quantification of a machine tool's (MT) energy consumption during all life phases. Scope of the presented tool is the fast and efficient estimation of a MT's cumulated energy demand and the systematic derivation of improvement measures regarding ecological performance. This work will present a framework, as well as the required calculations for this task. Using model and rule-based procedures, only a minimal set of input parameters is required to identify the hot-spots regarding energy consumption and improvement potential. Applications of this tool as well as a systematic approach to derive measures to increase the energy efficiency based on the output of the tool are presented on practice-oriented examples from industry